Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables

Mario Hernandez Tinoco and Nick Wilson

International Review of Financial Analysis, 2013, vol. 30, issue C, 394-419

Abstract: Using a sample of 23,218company-year observations of listed companies during the period 1980–2011, the paper investigates empirically the utility of combining accounting, market-based and macro-economic data to explain corporate credit risk. The paper develops risk models for listed companies that predict financial distress and bankruptcy. The estimated models use a combination of accounting data, stock market information and proxies for changes in the macro-economic environment. The purpose is to produce models with predictive accuracy, practical value and macro dependent dynamics that have relevance for stress testing. The results show the utility of combining accounting, market and macro-economic data in financial distress prediction models for listed companies. The performance of the estimated models is benchmarked against models built using a neural network (MLP) and against Altman's (1968) original Z-score specification.

Keywords: Bankruptcy; Listed companies; Financial distress; Logit regression; Neural networks (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (112)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:30:y:2013:i:c:p:394-419

DOI: 10.1016/j.irfa.2013.02.013

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